Overview

Dataset statistics

Number of variables32
Number of observations591
Missing cells62
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory519.8 KiB
Average record size in memory900.7 B

Variable types

NUM21
CAT11

Reproduction

Analysis started2020-08-15 04:57:22.648686
Analysis finished2020-08-15 04:59:10.085534
Duration1 minute and 47.44 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

count_of_testers has a high cardinality: 131 distinct values High cardinality
count_of_offers has a high cardinality: 55 distinct values High cardinality
Percent Asian has a high cardinality: 61 distinct values High cardinality
Percent Black has a high cardinality: 97 distinct values High cardinality
Percent Hispanic has a high cardinality: 95 distinct values High cardinality
Percent Black / Hispanic has a high cardinality: 88 distinct values High cardinality
Percent White has a high cardinality: 74 distinct values High cardinality
Average Math Proficiency is highly correlated with Average ELA Proficiency and 1 other fieldsHigh correlation
Average ELA Proficiency is highly correlated with Average Math Proficiency and 1 other fieldsHigh correlation
Grade 7 ELA - All Students Tested is highly correlated with count_of_students_in_hs_admissions and 1 other fieldsHigh correlation
count_of_students_in_hs_admissions is highly correlated with Grade 7 ELA - All Students Tested and 1 other fieldsHigh correlation
Grade 7 Math - All Students Tested is highly correlated with count_of_students_in_hs_admissions and 1 other fieldsHigh correlation
Grade 7 Math 4s - All Students is highly correlated with Grade 7 ELA 4s - All Students and 1 other fieldsHigh correlation
Grade 7 ELA 4s - All Students is highly correlated with Grade 7 Math 4s - All Students and 1 other fieldsHigh correlation
AvgMark is highly correlated with Average ELA Proficiency and 1 other fieldsHigh correlation
AvgScore4 is highly correlated with Grade 7 ELA 4s - All Students and 1 other fieldsHigh correlation
PctScore4 is highly correlated with PctScore4ELA and 1 other fieldsHigh correlation
PctScore4ELA is highly correlated with PctScore4High correlation
PctScore4Math is highly correlated with PctScore4High correlation
PctScore4ELA has 20 (3.4%) missing values Missing
PctScore4Math has 21 (3.6%) missing values Missing
PctScore4 has 21 (3.6%) missing values Missing
feeder_school_dbn has unique values Unique
feeder_school_name has unique values Unique
Location Code has unique values Unique
Average ELA Proficiency has 6 (1.0%) zeros Zeros
Average Math Proficiency has 6 (1.0%) zeros Zeros
Grade 7 ELA - All Students Tested has 20 (3.4%) zeros Zeros
Grade 7 ELA 4s - All Students has 89 (15.1%) zeros Zeros
Grade 7 Math - All Students Tested has 21 (3.6%) zeros Zeros
Grade 7 Math 4s - All Students has 139 (23.5%) zeros Zeros
NumTestTakers has 55 (9.3%) zeros Zeros
NumSpecializedOffers has 470 (79.5%) zeros Zeros
PctOffersByStudent has 470 (79.5%) zeros Zeros
PerDidSHSAT has 55 (9.3%) zeros Zeros
AvgMark has 6 (1.0%) zeros Zeros
PctScore4ELA has 69 (11.7%) zeros Zeros
PctScore4Math has 118 (20.0%) zeros Zeros
AvgScore4 has 64 (10.8%) zeros Zeros
PctScore4 has 68 (11.5%) zeros Zeros

Variables

feeder_school_dbn
Categorical

UNIQUE

Distinct count591
Unique (%)100.0%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
04M171
 
1
31R007
 
1
84Q705
 
1
03M334
 
1
21K239
 
1
Other values (586)
586
ValueCountFrequency (%) 
04M17110.2%
 
31R00710.2%
 
84Q70510.2%
 
03M33410.2%
 
21K23910.2%
 
84K74210.2%
 
01M14010.2%
 
15K83910.2%
 
13K49210.2%
 
03M16510.2%
 
17K34010.2%
 
21K09810.2%
 
27Q04710.2%
 
06M36610.2%
 
14K05010.2%
 
14K15710.2%
 
13K26610.2%
 
27Q30910.2%
 
08X12510.2%
 
21K28810.2%
 
29Q28910.2%
 
12X21710.2%
 
08X10110.2%
 
12X12910.2%
 
27Q11410.2%
 
Other values (566)56695.8%
 
2020-08-14T21:59:10.231645image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories (?)2
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
246613.1%
 
142512.0%
 
041811.8%
 
33168.9%
 
43158.9%
 
83068.6%
 
72025.7%
 
K1895.3%
 
51744.9%
 
61724.9%
 
91614.5%
 
X1474.1%
 
M1263.6%
 
Q1123.2%
 
R170.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number295583.3%
 
Uppercase Letter59116.7%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
246615.8%
 
142514.4%
 
041814.1%
 
331610.7%
 
431510.7%
 
830610.4%
 
72026.8%
 
51745.9%
 
61725.8%
 
91615.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
K18932.0%
 
X14724.9%
 
M12621.3%
 
Q11219.0%
 
R172.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common295583.3%
 
Latin59116.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
246615.8%
 
142514.4%
 
041814.1%
 
331610.7%
 
431510.7%
 
830610.4%
 
72026.8%
 
51745.9%
 
61725.8%
 
91615.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
K18932.0%
 
X14724.9%
 
M12621.3%
 
Q11219.0%
 
R172.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3546100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
246613.1%
 
142512.0%
 
041811.8%
 
33168.9%
 
43158.9%
 
83068.6%
 
72025.7%
 
K1895.3%
 
51744.9%
 
61724.9%
 
91614.5%
 
X1474.1%
 
M1263.6%
 
Q1123.2%
 
R170.5%
 

feeder_school_name
Categorical

UNIQUE

Distinct count591
Unique (%)100.0%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
M.S. 250 WEST SIDE COLLABORATIVE MIDDLE SCHOOL
 
1
M.S. 158 MARIE CURIE
 
1
SCHOOL OF THE FUTURE HIGH SCHOOL
 
1
EAGLE ACADEMY FOR YOUNG MEN III
 
1
SATELLITE EAST MIDDLE SCHOOL
 
1
Other values (586)
586
ValueCountFrequency (%) 
M.S. 250 WEST SIDE COLLABORATIVE MIDDLE SCHOOL10.2%
 
M.S. 158 MARIE CURIE10.2%
 
SCHOOL OF THE FUTURE HIGH SCHOOL10.2%
 
EAGLE ACADEMY FOR YOUNG MEN III10.2%
 
SATELLITE EAST MIDDLE SCHOOL10.2%
 
EMOLIOR ACADEMY10.2%
 
J.H.S. 202 ROBERT H. GODDARD10.2%
 
P.S./M.S. 004 CROTONA PARK WEST10.2%
 
ATMOSPHERE CHARTER SCHOOL10.2%
 
DOCK STREET SCHOOL FOR STEAM STUDIES10.2%
 
RONALD EDMONDS LEARNING CENTER II10.2%
 
DR. RICHARD IZQUIERDO HEALTH AND SCIENCE CHARTER SCHOOL10.2%
 
ACHIEVEMENT FIRST APOLLO CHARTER SCHOOL10.2%
 
SCIENCE AND TECHNOLOGY ACADEMY: A MOTT HALL SCHOOL10.2%
 
P.S. 184M SHUANG WEN10.2%
 
FUTURE LEADERS INSTITUTE CHARTER SCHOOL10.2%
 
BRONX WRITING ACADEMY10.2%
 
J.H.S. 292 MARGARET S. DOUGLAS10.2%
 
RIVERDALE AVENUE MIDDLE SCHOOL10.2%
 
LEADERSHIP PREP CANARSIE CHARTER SCHOOL (LEADERSHIP PREP CS 4)10.2%
 
P.S. 6610.2%
 
J.H.S. 194 WILLIAM CARR10.2%
 
OCEAN HILL COLLEGIATE CHARTER SCHOOL10.2%
 
SUCCESS ACADEMY CHARTER SCHOOL - BRONX 110.2%
 
P.S. 096 JOSEPH LANZETTA10.2%
 
Other values (566)56695.8%
 
2020-08-14T21:59:10.448081image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length78
Median length29
Mean length30.51945854
Min length5

Overview of Unicode Properties

Unique unicode characters48
Unique unicode categories (?)7
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
224712.5%
 
E14528.1%
 
O13257.3%
 
A11826.6%
 
S11406.3%
 
R10535.8%
 
L9595.3%
 
C9015.0%
 
H8364.6%
 
I7744.3%
 
N7194.0%
 
T7103.9%
 
.7053.9%
 
D5252.9%
 
M4822.7%
 
P3852.1%
 
Y3161.8%
 
G2461.4%
 
U2441.4%
 
B1931.1%
 
F1650.9%
 
11460.8%
 
W1410.8%
 
21390.8%
 
01160.6%
 
Other values (23)9365.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1414578.4%
 
Space Separator224712.5%
 
Other Punctuation8034.5%
 
Decimal Number8014.4%
 
Dash Punctuation250.1%
 
Open Punctuation8< 0.1%
 
Close Punctuation8< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E145210.3%
 
O13259.4%
 
A11828.4%
 
S11408.1%
 
R10537.4%
 
L9596.8%
 
C9016.4%
 
H8365.9%
 
I7745.5%
 
N7195.1%
 
T7105.0%
 
D5253.7%
 
M4823.4%
 
P3852.7%
 
Y3162.2%
 
G2461.7%
 
U2441.7%
 
B1931.4%
 
F1651.2%
 
W1411.0%
 
K1090.8%
 
J970.7%
 
V920.7%
 
X630.4%
 
Q180.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.70587.8%
 
/374.6%
 
,222.7%
 
&182.2%
 
:101.2%
 
'91.1%
 
\10.1%
 
?10.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2247100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
114618.2%
 
213917.4%
 
011614.5%
 
38010.0%
 
8688.5%
 
4617.6%
 
7536.6%
 
5506.2%
 
9496.1%
 
6394.9%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-25100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(8100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)8100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1414578.4%
 
Common389221.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E145210.3%
 
O13259.4%
 
A11828.4%
 
S11408.1%
 
R10537.4%
 
L9596.8%
 
C9016.4%
 
H8365.9%
 
I7745.5%
 
N7195.1%
 
T7105.0%
 
D5253.7%
 
M4823.4%
 
P3852.7%
 
Y3162.2%
 
G2461.7%
 
U2441.7%
 
B1931.4%
 
F1651.2%
 
W1411.0%
 
K1090.8%
 
J970.7%
 
V920.7%
 
X630.4%
 
Q180.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
224757.7%
 
.70518.1%
 
11463.8%
 
21393.6%
 
01163.0%
 
3802.1%
 
8681.7%
 
4611.6%
 
7531.4%
 
5501.3%
 
9491.3%
 
6391.0%
 
/371.0%
 
-250.6%
 
,220.6%
 
&180.5%
 
:100.3%
 
'90.2%
 
(80.2%
 
)80.2%
 
\1< 0.1%
 
?1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII18037100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
224712.5%
 
E14528.1%
 
O13257.3%
 
A11826.6%
 
S11406.3%
 
R10535.8%
 
L9595.3%
 
C9015.0%
 
H8364.6%
 
I7744.3%
 
N7194.0%
 
T7103.9%
 
.7053.9%
 
D5252.9%
 
M4822.7%
 
P3852.1%
 
Y3161.8%
 
G2461.4%
 
U2441.4%
 
B1931.1%
 
F1650.9%
 
11460.8%
 
W1410.8%
 
21390.8%
 
01160.6%
 
Other values (23)9365.2%
 

count_of_students_in_hs_admissions
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count220
Unique (%)37.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.12013536379018
Minimum9.0
Maximum769.0
Zeros0
Zeros (%)0.0%
Memory size29.2 KiB
2020-08-14T21:59:10.624818image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile32
Q163
median88
Q3132.5
95-th percentile413.5
Maximum769
Range760
Interquartile range (IQR)69.5

Descriptive statistics

Standard deviation118.1216299
Coefficient of variation (CV)0.9292125872
Kurtosis6.876898316
Mean127.1201354
Median Absolute Deviation (MAD)30
Skewness2.546870897
Sum75128
Variance13952.71944
2020-08-14T21:59:10.790394image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
87111.9%
 
94101.7%
 
10291.5%
 
7191.5%
 
7891.5%
 
8491.5%
 
7681.4%
 
9681.4%
 
5681.4%
 
6971.2%
 
8271.2%
 
4771.2%
 
5071.2%
 
8371.2%
 
6371.2%
 
6271.2%
 
8871.2%
 
5971.2%
 
5171.2%
 
9971.2%
 
8561.0%
 
6061.0%
 
5561.0%
 
7461.0%
 
7761.0%
 
Other values (195)40368.2%
 
ValueCountFrequency (%) 
920.3%
 
1520.3%
 
1620.3%
 
2120.3%
 
2230.5%
 
2410.2%
 
2510.2%
 
2730.5%
 
2810.2%
 
2940.7%
 
ValueCountFrequency (%) 
76910.2%
 
71610.2%
 
68410.2%
 
64210.2%
 
64010.2%
 
62910.2%
 
58110.2%
 
58010.2%
 
56210.2%
 
54010.2%
 

count_of_testers
Categorical

HIGH CARDINALITY

Distinct count131
Unique (%)22.2%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
0-5
 
55
6
 
19
9
 
17
7
 
17
10
 
16
Other values (126)
467
ValueCountFrequency (%) 
0-5559.3%
 
6193.2%
 
9172.9%
 
7172.9%
 
10162.7%
 
16152.5%
 
15152.5%
 
13152.5%
 
24152.5%
 
21142.4%
 
8142.4%
 
20132.2%
 
12132.2%
 
17132.2%
 
29132.2%
 
18132.2%
 
35122.0%
 
27122.0%
 
11122.0%
 
22111.9%
 
42101.7%
 
26101.7%
 
23101.7%
 
19101.7%
 
1471.2%
 
Other values (106)22037.2%
 
2020-08-14T21:59:11.023259image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.081218274
Min length1

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
122418.2%
 
217614.3%
 
512610.2%
 
312610.2%
 
01179.5%
 
6937.6%
 
4927.5%
 
7877.1%
 
9756.1%
 
8594.8%
 
-554.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number117595.5%
 
Dash Punctuation554.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
122419.1%
 
217615.0%
 
512610.7%
 
312610.7%
 
011710.0%
 
6937.9%
 
4927.8%
 
7877.4%
 
9756.4%
 
8595.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-55100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1230100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
122418.2%
 
217614.3%
 
512610.2%
 
312610.2%
 
01179.5%
 
6937.6%
 
4927.5%
 
7877.1%
 
9756.1%
 
8594.8%
 
-554.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1230100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
122418.2%
 
217614.3%
 
512610.2%
 
312610.2%
 
01179.5%
 
6937.6%
 
4927.5%
 
7877.1%
 
9756.1%
 
8594.8%
 
-554.5%
 

count_of_offers
Categorical

HIGH CARDINALITY

Distinct count55
Unique (%)9.3%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
0-5
470
7
 
14
9
 
9
8
 
8
6
 
7
Other values (50)
 
83
ValueCountFrequency (%) 
0-547079.5%
 
7142.4%
 
991.5%
 
881.4%
 
671.2%
 
2750.8%
 
1440.7%
 
1740.7%
 
2640.7%
 
1940.7%
 
2340.7%
 
2030.5%
 
1130.5%
 
5330.5%
 
1320.3%
 
1820.3%
 
4620.3%
 
2220.3%
 
1520.3%
 
2120.3%
 
2920.3%
 
9520.3%
 
3010.2%
 
7510.2%
 
1010.2%
 
Other values (30)305.1%
 
2020-08-14T21:59:11.266802image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.742808799
Min length1

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
548630.0%
 
048129.7%
 
-47029.0%
 
1382.3%
 
2332.0%
 
7291.8%
 
9231.4%
 
3171.0%
 
6161.0%
 
8150.9%
 
4130.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number115171.0%
 
Dash Punctuation47029.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
548642.2%
 
048141.8%
 
1383.3%
 
2332.9%
 
7292.5%
 
9232.0%
 
3171.5%
 
6161.4%
 
8151.3%
 
4131.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-470100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1621100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
548630.0%
 
048129.7%
 
-47029.0%
 
1382.3%
 
2332.0%
 
7291.8%
 
9231.4%
 
3171.0%
 
6161.0%
 
8150.9%
 
4130.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1621100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
548630.0%
 
048129.7%
 
-47029.0%
 
1382.3%
 
2332.0%
 
7291.8%
 
9231.4%
 
3171.0%
 
6161.0%
 
8150.9%
 
4130.8%
 

Location Code
Categorical

UNIQUE

Distinct count591
Unique (%)100.0%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
04M171
 
1
31R007
 
1
84Q705
 
1
03M334
 
1
21K239
 
1
Other values (586)
586
ValueCountFrequency (%) 
04M17110.2%
 
31R00710.2%
 
84Q70510.2%
 
03M33410.2%
 
21K23910.2%
 
84K74210.2%
 
01M14010.2%
 
15K83910.2%
 
13K49210.2%
 
03M16510.2%
 
17K34010.2%
 
21K09810.2%
 
27Q04710.2%
 
06M36610.2%
 
14K05010.2%
 
14K15710.2%
 
13K26610.2%
 
27Q30910.2%
 
08X12510.2%
 
21K28810.2%
 
29Q28910.2%
 
12X21710.2%
 
08X10110.2%
 
12X12910.2%
 
27Q11410.2%
 
Other values (566)56695.8%
 
2020-08-14T21:59:11.486077image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories (?)2
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
246613.1%
 
142512.0%
 
041811.8%
 
33168.9%
 
43158.9%
 
83068.6%
 
72025.7%
 
K1895.3%
 
51744.9%
 
61724.9%
 
91614.5%
 
X1474.1%
 
M1263.6%
 
Q1123.2%
 
R170.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number295583.3%
 
Uppercase Letter59116.7%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
246615.8%
 
142514.4%
 
041814.1%
 
331610.7%
 
431510.7%
 
830610.4%
 
72026.8%
 
51745.9%
 
61725.8%
 
91615.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
K18932.0%
 
X14724.9%
 
M12621.3%
 
Q11219.0%
 
R172.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common295583.3%
 
Latin59116.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
246615.8%
 
142514.4%
 
041814.1%
 
331610.7%
 
431510.7%
 
830610.4%
 
72026.8%
 
51745.9%
 
61725.8%
 
91615.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
K18932.0%
 
X14724.9%
 
M12621.3%
 
Q11219.0%
 
R172.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3546100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
246613.1%
 
142512.0%
 
041811.8%
 
33168.9%
 
43158.9%
 
83068.6%
 
72025.7%
 
K1895.3%
 
51744.9%
 
61724.9%
 
91614.5%
 
X1474.1%
 
M1263.6%
 
Q1123.2%
 
R170.5%
 

District
Real number (ℝ≥0)

Distinct count32
Unique (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.323181049069374
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Memory size29.2 KiB
2020-08-14T21:59:11.650030image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median14
Q323
95-th percentile30
Maximum32
Range31
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.172108829
Coefficient of variation (CV)0.5985773319
Kurtosis-1.19926557
Mean15.32318105
Median Absolute Deviation (MAD)8
Skewness0.2257198947
Sum9056
Variance84.12758037
2020-08-14T21:59:11.818856image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
9325.4%
 
10294.9%
 
11264.4%
 
6244.1%
 
27244.1%
 
23244.1%
 
3244.1%
 
2233.9%
 
5223.7%
 
4213.6%
 
12213.6%
 
30203.4%
 
8203.4%
 
17203.4%
 
7193.2%
 
13193.2%
 
19193.2%
 
31172.9%
 
21172.9%
 
29172.9%
 
15162.7%
 
24162.7%
 
20152.5%
 
18142.4%
 
25142.4%
 
Other values (7)7813.2%
 
ValueCountFrequency (%) 
1122.0%
 
2233.9%
 
3244.1%
 
4213.6%
 
5223.7%
 
6244.1%
 
7193.2%
 
8203.4%
 
9325.4%
 
10294.9%
 
ValueCountFrequency (%) 
32122.0%
 
31172.9%
 
30203.4%
 
29172.9%
 
28132.2%
 
27244.1%
 
2681.4%
 
25142.4%
 
24162.7%
 
23244.1%
 

Latitude
Real number (ℝ≥0)

Distinct count504
Unique (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.74058076818952
Minimum40.507803
Maximum40.899321
Zeros0
Zeros (%)0.0%
Memory size29.2 KiB
2020-08-14T21:59:11.987287image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum40.507803
5-th percentile40.601174
Q140.672226
median40.735735
Q340.821218
95-th percentile40.864049
Maximum40.899321
Range0.391518
Interquartile range (IQR)0.148992

Descriptive statistics

Standard deviation0.08654233741
Coefficient of variation (CV)0.00212422935
Kurtosis-1.115834578
Mean40.74058077
Median Absolute Deviation (MAD)0.075119
Skewness-0.116202987
Sum24077.68323
Variance0.007489576164
2020-08-14T21:59:12.154499image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
40.69691330.5%
 
40.83940830.5%
 
40.67811330.5%
 
40.79168530.5%
 
40.8620630.5%
 
40.85663530.5%
 
40.82208430.5%
 
40.67698230.5%
 
40.8608230.5%
 
40.88604330.5%
 
40.81728220.3%
 
40.70081820.3%
 
40.65576320.3%
 
40.8438520.3%
 
40.80267820.3%
 
40.85950620.3%
 
40.83227820.3%
 
40.72419120.3%
 
40.81597120.3%
 
40.66485820.3%
 
40.67270620.3%
 
40.84503120.3%
 
40.81350720.3%
 
40.74953920.3%
 
40.67506120.3%
 
Other values (479)53189.8%
 
ValueCountFrequency (%) 
40.50780310.2%
 
40.5240910.2%
 
40.54436910.2%
 
40.54579310.2%
 
40.5742710.2%
 
40.57709410.2%
 
40.57827710.2%
 
40.5786610.2%
 
40.57960210.2%
 
40.5798110.2%
 
ValueCountFrequency (%) 
40.89932110.2%
 
40.88770110.2%
 
40.88604330.5%
 
40.88532810.2%
 
40.88306410.2%
 
40.88302210.2%
 
40.88063420.3%
 
40.8798410.2%
 
40.8795610.2%
 
40.87717910.2%
 

Longitude
Real number (ℝ)

Distinct count502
Unique (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.91621322842641
Minimum-74.24322099999999
Maximum-73.713022
Zeros0
Zeros (%)0.0%
Memory size29.2 KiB
2020-08-14T21:59:12.339348image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-74.243221
5-th percentile-74.0044105
Q1-73.95408
median-73.920032
Q3-73.8847405
95-th percentile-73.77958
Maximum-73.713022
Range0.530199
Interquartile range (IQR)0.0693395

Descriptive statistics

Standard deviation0.07288724708
Coefficient of variation (CV)-0.0009860792903
Kurtosis2.281007412
Mean-73.91621323
Median Absolute Deviation (MAD)0.034868
Skewness-0.2317734609
Sum-43684.48202
Variance0.005312550787
2020-08-14T21:59:12.506751image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-73.93006630.5%
 
-73.84057630.5%
 
-73.78639630.5%
 
-73.88383830.5%
 
-73.91521130.5%
 
-73.9707730.5%
 
-73.86458830.5%
 
-73.93577230.5%
 
-73.89403830.5%
 
-73.84304230.5%
 
-73.94580820.3%
 
-73.92599920.3%
 
-73.90844720.3%
 
-73.97808520.3%
 
-73.94445720.3%
 
-73.9659520.3%
 
-73.78968920.3%
 
-73.85797620.3%
 
-73.93237820.3%
 
-73.97737620.3%
 
-73.94723820.3%
 
-73.94211920.3%
 
-73.86006220.3%
 
-73.95146220.3%
 
-73.93932320.3%
 
Other values (477)53189.8%
 
ValueCountFrequency (%) 
-74.24322110.2%
 
-74.19720110.2%
 
-74.18618310.2%
 
-74.17972310.2%
 
-74.16443210.2%
 
-74.1593610.2%
 
-74.15853210.2%
 
-74.14556110.2%
 
-74.14529510.2%
 
-74.13707110.2%
 
ValueCountFrequency (%) 
-73.71302210.2%
 
-73.72758110.2%
 
-73.72838510.2%
 
-73.73139410.2%
 
-73.73442610.2%
 
-73.73443410.2%
 
-73.73674110.2%
 
-73.74015110.2%
 
-73.74343810.2%
 
-73.74657210.2%
 

Percent ELL
Categorical

Distinct count47
Unique (%)8.0%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
1%
 
48
2%
 
46
3%
 
43
4%
 
37
7%
 
34
Other values (42)
383
ValueCountFrequency (%) 
1%488.1%
 
2%467.8%
 
3%437.3%
 
4%376.3%
 
7%345.8%
 
6%315.2%
 
5%305.1%
 
8%274.6%
 
11%274.6%
 
9%254.2%
 
0%254.2%
 
10%203.4%
 
19%162.7%
 
14%142.4%
 
15%142.4%
 
12%142.4%
 
18%132.2%
 
13%132.2%
 
17%122.0%
 
16%122.0%
 
21%91.5%
 
31%91.5%
 
22%81.4%
 
27%61.0%
 
20%61.0%
 
Other values (22)528.8%
 
2020-08-14T21:59:12.725161image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.414551607
Min length2

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
%59141.4%
 
125417.8%
 
21238.6%
 
3916.4%
 
4664.6%
 
0543.8%
 
7533.7%
 
6533.7%
 
5513.6%
 
9483.4%
 
8433.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number83658.6%
 
Other Punctuation59141.4%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
125430.4%
 
212314.7%
 
39110.9%
 
4667.9%
 
0546.5%
 
7536.3%
 
6536.3%
 
5516.1%
 
9485.7%
 
8435.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
%591100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1427100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
%59141.4%
 
125417.8%
 
21238.6%
 
3916.4%
 
4664.6%
 
0543.8%
 
7533.7%
 
6533.7%
 
5513.6%
 
9483.4%
 
8433.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1427100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
%59141.4%
 
125417.8%
 
21238.6%
 
3916.4%
 
4664.6%
 
0543.8%
 
7533.7%
 
6533.7%
 
5513.6%
 
9483.4%
 
8433.0%
 

Percent Asian
Categorical

HIGH CARDINALITY

Distinct count61
Unique (%)10.3%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
1%
134
2%
74
0%
71
3%
 
42
4%
 
30
Other values (56)
240
ValueCountFrequency (%) 
1%13422.7%
 
2%7412.5%
 
0%7112.0%
 
3%427.1%
 
4%305.1%
 
5%274.6%
 
6%172.9%
 
7%162.7%
 
8%101.7%
 
9%91.5%
 
11%91.5%
 
18%81.4%
 
12%71.2%
 
10%71.2%
 
29%61.0%
 
14%61.0%
 
15%61.0%
 
17%61.0%
 
33%61.0%
 
23%61.0%
 
20%50.8%
 
28%50.8%
 
27%50.8%
 
21%50.8%
 
16%40.7%
 
Other values (36)7011.8%
 
2020-08-14T21:59:12.924772image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.272419628
Min length2

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
%59144.0%
 
121215.8%
 
21309.7%
 
0926.9%
 
3826.1%
 
4654.8%
 
5473.5%
 
7372.8%
 
6362.7%
 
8272.0%
 
9241.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number75256.0%
 
Other Punctuation59144.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
121228.2%
 
213017.3%
 
09212.2%
 
38210.9%
 
4658.6%
 
5476.2%
 
7374.9%
 
6364.8%
 
8273.6%
 
9243.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
%591100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1343100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
%59144.0%
 
121215.8%
 
21309.7%
 
0926.9%
 
3826.1%
 
4654.8%
 
5473.5%
 
7372.8%
 
6362.7%
 
8272.0%
 
9241.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1343100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
%59144.0%
 
121215.8%
 
21309.7%
 
0926.9%
 
3826.1%
 
4654.8%
 
5473.5%
 
7372.8%
 
6362.7%
 
8272.0%
 
9241.8%
 

Percent Black
Categorical

HIGH CARDINALITY

Distinct count97
Unique (%)16.4%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
1%
 
27
3%
 
24
4%
 
15
2%
 
14
25%
 
14
Other values (92)
497
ValueCountFrequency (%) 
1%274.6%
 
3%244.1%
 
4%152.5%
 
2%142.4%
 
25%142.4%
 
26%132.2%
 
40%122.0%
 
11%111.9%
 
7%111.9%
 
10%111.9%
 
6%111.9%
 
8%111.9%
 
13%101.7%
 
21%101.7%
 
12%101.7%
 
18%101.7%
 
22%101.7%
 
5%101.7%
 
17%91.5%
 
28%91.5%
 
84%91.5%
 
9%91.5%
 
30%91.5%
 
35%91.5%
 
27%91.5%
 
Other values (72)29449.7%
 
2020-08-14T21:59:13.118576image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.766497462
Min length2

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
%59136.1%
 
11579.6%
 
21549.4%
 
31177.2%
 
81126.9%
 
51026.2%
 
6996.1%
 
4945.7%
 
7875.3%
 
0643.9%
 
9583.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number104463.9%
 
Other Punctuation59136.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
115715.0%
 
215414.8%
 
311711.2%
 
811210.7%
 
51029.8%
 
6999.5%
 
4949.0%
 
7878.3%
 
0646.1%
 
9585.6%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
%591100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1635100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
%59136.1%
 
11579.6%
 
21549.4%
 
31177.2%
 
81126.9%
 
51026.2%
 
6996.1%
 
4945.7%
 
7875.3%
 
0643.9%
 
9583.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1635100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
%59136.1%
 
11579.6%
 
21549.4%
 
31177.2%
 
81126.9%
 
51026.2%
 
6996.1%
 
4945.7%
 
7875.3%
 
0643.9%
 
9583.5%
 

Percent Hispanic
Categorical

HIGH CARDINALITY

Distinct count95
Unique (%)16.1%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
11%
 
16
14%
 
13
12%
 
13
33%
 
13
67%
 
12
Other values (90)
524
ValueCountFrequency (%) 
11%162.7%
 
14%132.2%
 
12%132.2%
 
33%132.2%
 
67%122.0%
 
16%111.9%
 
28%111.9%
 
18%111.9%
 
29%111.9%
 
72%111.9%
 
15%111.9%
 
23%101.7%
 
10%101.7%
 
32%101.7%
 
63%101.7%
 
31%101.7%
 
19%101.7%
 
13%91.5%
 
77%91.5%
 
9%91.5%
 
27%91.5%
 
24%91.5%
 
21%91.5%
 
25%81.4%
 
85%81.4%
 
Other values (70)32855.5%
 
2020-08-14T21:59:13.333703image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.918781726
Min length2

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
%59134.3%
 
117210.0%
 
21538.9%
 
31388.0%
 
61237.1%
 
71136.6%
 
51106.4%
 
41056.1%
 
8905.2%
 
9834.8%
 
0472.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number113465.7%
 
Other Punctuation59134.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
117215.2%
 
215313.5%
 
313812.2%
 
612310.8%
 
711310.0%
 
51109.7%
 
41059.3%
 
8907.9%
 
9837.3%
 
0474.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
%591100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1725100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
%59134.3%
 
117210.0%
 
21538.9%
 
31388.0%
 
61237.1%
 
71136.6%
 
51106.4%
 
41056.1%
 
8905.2%
 
9834.8%
 
0472.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1725100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
%59134.3%
 
117210.0%
 
21538.9%
 
31388.0%
 
61237.1%
 
71136.6%
 
51106.4%
 
41056.1%
 
8905.2%
 
9834.8%
 
0472.7%
 

Percent Black / Hispanic
Categorical

HIGH CARDINALITY

Distinct count88
Unique (%)14.9%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
98%
70
97%
 
61
96%
 
50
95%
 
43
94%
 
30
Other values (83)
337
ValueCountFrequency (%) 
98%7011.8%
 
97%6110.3%
 
96%508.5%
 
95%437.3%
 
94%305.1%
 
99%213.6%
 
93%183.0%
 
92%162.7%
 
91%152.5%
 
87%111.9%
 
90%101.7%
 
66%81.4%
 
27%71.2%
 
40%71.2%
 
78%61.0%
 
83%61.0%
 
63%61.0%
 
61%61.0%
 
13%61.0%
 
89%61.0%
 
29%61.0%
 
79%50.8%
 
32%50.8%
 
28%50.8%
 
31%50.8%
 
Other values (63)16227.4%
 
2020-08-14T21:59:13.562918image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.001692047
Min length2

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
%59133.3%
 
937821.3%
 
81417.9%
 
71327.4%
 
61096.1%
 
5935.2%
 
3774.3%
 
4774.3%
 
1734.1%
 
2663.7%
 
0372.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number118366.7%
 
Other Punctuation59133.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
937832.0%
 
814111.9%
 
713211.2%
 
61099.2%
 
5937.9%
 
3776.5%
 
4776.5%
 
1736.2%
 
2665.6%
 
0373.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
%591100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1774100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
%59133.3%
 
937821.3%
 
81417.9%
 
71327.4%
 
61096.1%
 
5935.2%
 
3774.3%
 
4774.3%
 
1734.1%
 
2663.7%
 
0372.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1774100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
%59133.3%
 
937821.3%
 
81417.9%
 
71327.4%
 
61096.1%
 
5935.2%
 
3774.3%
 
4774.3%
 
1734.1%
 
2663.7%
 
0372.1%
 

Percent White
Categorical

HIGH CARDINALITY

Distinct count74
Unique (%)12.5%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
1%
182
2%
71
0%
62
3%
 
41
4%
 
25
Other values (69)
210
ValueCountFrequency (%) 
1%18230.8%
 
2%7112.0%
 
0%6210.5%
 
3%416.9%
 
4%254.2%
 
5%101.7%
 
7%81.4%
 
27%71.2%
 
16%71.2%
 
8%71.2%
 
6%61.0%
 
20%61.0%
 
42%61.0%
 
13%61.0%
 
12%61.0%
 
10%61.0%
 
11%50.8%
 
9%50.8%
 
18%50.8%
 
39%50.8%
 
32%50.8%
 
14%50.8%
 
23%40.7%
 
38%40.7%
 
36%40.7%
 
Other values (49)9315.7%
 
2020-08-14T21:59:13.790925image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.294416244
Min length2

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
%59143.6%
 
124618.1%
 
21279.4%
 
3946.9%
 
0816.0%
 
4624.6%
 
5423.1%
 
6372.7%
 
7302.2%
 
8272.0%
 
9191.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number76556.4%
 
Other Punctuation59143.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
124632.2%
 
212716.6%
 
39412.3%
 
08110.6%
 
4628.1%
 
5425.5%
 
6374.8%
 
7303.9%
 
8273.5%
 
9192.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
%591100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1356100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
%59143.6%
 
124618.1%
 
21279.4%
 
3946.9%
 
0816.0%
 
4624.6%
 
5423.1%
 
6372.7%
 
7302.2%
 
8272.0%
 
9191.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1356100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
%59143.6%
 
124618.1%
 
21279.4%
 
3946.9%
 
0816.0%
 
4624.6%
 
5423.1%
 
6372.7%
 
7302.2%
 
8272.0%
 
9191.4%
 

Average ELA Proficiency
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count150
Unique (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5174111675126905
Minimum0.0
Maximum3.93
Zeros6
Zeros (%)1.0%
Memory size29.2 KiB
2020-08-14T21:59:13.982191image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.085
Q12.26
median2.46
Q32.73
95-th percentile3.255
Maximum3.93
Range3.93
Interquartile range (IQR)0.47

Descriptive statistics

Standard deviation0.4532805826
Coefficient of variation (CV)0.1800582235
Kurtosis8.781746706
Mean2.517411168
Median Absolute Deviation (MAD)0.23
Skewness-1.040948877
Sum1487.79
Variance0.2054632866
2020-08-14T21:59:14.143064image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.27172.9%
 
2.32122.0%
 
2.24111.9%
 
2.37101.7%
 
2.48101.7%
 
2.26101.7%
 
2.23101.7%
 
2.64101.7%
 
2.4391.5%
 
2.3891.5%
 
2.3691.5%
 
2.1691.5%
 
2.4791.5%
 
2.1481.4%
 
2.4681.4%
 
2.1981.4%
 
2.3181.4%
 
2.4981.4%
 
2.1781.4%
 
2.4181.4%
 
2.1571.2%
 
2.4571.2%
 
2.2171.2%
 
2.5171.2%
 
2.371.2%
 
Other values (125)36561.8%
 
ValueCountFrequency (%) 
061.0%
 
1.8110.2%
 
1.910.2%
 
1.9110.2%
 
1.9610.2%
 
1.9710.2%
 
1.9820.3%
 
210.2%
 
2.0310.2%
 
2.0430.5%
 
ValueCountFrequency (%) 
3.9310.2%
 
3.9110.2%
 
3.8320.3%
 
3.7510.2%
 
3.7310.2%
 
3.710.2%
 
3.6710.2%
 
3.6610.2%
 
3.6410.2%
 
3.6210.2%
 

Average Math Proficiency
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count179
Unique (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5983248730964466
Minimum0.0
Maximum4.19
Zeros6
Zeros (%)1.0%
Memory size29.2 KiB
2020-08-14T21:59:14.317577image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.98
Q12.22
median2.53
Q32.94
95-th percentile3.58
Maximum4.19
Range4.19
Interquartile range (IQR)0.72

Descriptive statistics

Standard deviation0.5530578197
Coefficient of variation (CV)0.212851682
Kurtosis3.958461016
Mean2.598324873
Median Absolute Deviation (MAD)0.35
Skewness-0.4219935105
Sum1535.61
Variance0.3058729519
2020-08-14T21:59:14.469971image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.15111.9%
 
2.13101.7%
 
2.2291.5%
 
2.3181.4%
 
2.381.4%
 
2.1781.4%
 
1.9871.2%
 
2.3271.2%
 
2.271.2%
 
2.4471.2%
 
2.5371.2%
 
2.2971.2%
 
2.5471.2%
 
2.2771.2%
 
2.1961.0%
 
2.0361.0%
 
2.0761.0%
 
2.9861.0%
 
2.161.0%
 
2.3361.0%
 
1.9761.0%
 
061.0%
 
2.8861.0%
 
2.3661.0%
 
2.4761.0%
 
Other values (154)41570.2%
 
ValueCountFrequency (%) 
061.0%
 
1.8920.3%
 
1.9120.3%
 
1.9220.3%
 
1.9410.2%
 
1.9540.7%
 
1.9610.2%
 
1.9761.0%
 
1.9871.2%
 
1.9950.8%
 
ValueCountFrequency (%) 
4.1910.2%
 
4.1510.2%
 
4.0710.2%
 
4.0310.2%
 
3.9910.2%
 
3.9710.2%
 
3.9510.2%
 
3.9420.3%
 
3.9210.2%
 
3.9110.2%
 

Grade 7 ELA - All Students Tested
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count213
Unique (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.35194585448393
Minimum0
Maximum698
Zeros20
Zeros (%)3.4%
Memory size29.2 KiB
2020-08-14T21:59:14.627218image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.5
Q153.5
median80
Q3119
95-th percentile376
Maximum698
Range698
Interquartile range (IQR)65.5

Descriptive statistics

Standard deviation112.5617932
Coefficient of variation (CV)0.9843452363
Kurtosis6.705131956
Mean114.3519459
Median Absolute Deviation (MAD)31
Skewness2.482817754
Sum67582
Variance12670.15728
2020-08-14T21:59:14.796586image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0203.4%
 
83132.2%
 
90122.0%
 
7591.5%
 
7291.5%
 
7891.5%
 
8091.5%
 
8681.4%
 
5581.4%
 
5681.4%
 
5081.4%
 
10281.4%
 
6681.4%
 
7371.2%
 
4171.2%
 
5271.2%
 
4961.0%
 
6861.0%
 
7161.0%
 
6461.0%
 
9161.0%
 
4861.0%
 
4461.0%
 
10161.0%
 
4361.0%
 
Other values (188)38765.5%
 
ValueCountFrequency (%) 
0203.4%
 
810.2%
 
1010.2%
 
1110.2%
 
1210.2%
 
1410.2%
 
1630.5%
 
2010.2%
 
2110.2%
 
2210.2%
 
ValueCountFrequency (%) 
69810.2%
 
67910.2%
 
64410.2%
 
64010.2%
 
63410.2%
 
57110.2%
 
55410.2%
 
54510.2%
 
50210.2%
 
49920.3%
 

Grade 7 ELA 4s - All Students
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count80
Unique (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.064297800338409
Minimum0
Maximum238
Zeros89
Zeros (%)15.1%
Memory size29.2 KiB
2020-08-14T21:59:14.977074image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q313
95-th percentile72.5
Maximum238
Range238
Interquartile range (IQR)12

Descriptive statistics

Standard deviation28.14482993
Coefficient of variation (CV)2.001154294
Kurtosis19.93488605
Mean14.0642978
Median Absolute Deviation (MAD)3
Skewness3.980651071
Sum8312
Variance792.131452
2020-08-14T21:59:15.123725image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
08915.1%
 
27713.0%
 
17612.9%
 
3406.8%
 
5315.2%
 
4305.1%
 
7264.4%
 
6203.4%
 
9162.7%
 
18101.7%
 
10101.7%
 
8101.7%
 
1191.5%
 
1591.5%
 
1981.4%
 
1381.4%
 
3271.2%
 
2071.2%
 
1261.0%
 
1661.0%
 
2261.0%
 
5040.7%
 
2440.7%
 
3340.7%
 
1740.7%
 
Other values (55)7412.5%
 
ValueCountFrequency (%) 
08915.1%
 
17612.9%
 
27713.0%
 
3406.8%
 
4305.1%
 
5315.2%
 
6203.4%
 
7264.4%
 
8101.7%
 
9162.7%
 
ValueCountFrequency (%) 
23810.2%
 
22610.2%
 
18610.2%
 
17510.2%
 
15710.2%
 
14910.2%
 
14310.2%
 
13510.2%
 
12710.2%
 
11410.2%
 

Grade 7 Math - All Students Tested
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count219
Unique (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.6751269035533
Minimum0
Maximum715
Zeros21
Zeros (%)3.6%
Memory size29.2 KiB
2020-08-14T21:59:15.281852image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q153.5
median81
Q3121.5
95-th percentile378.5
Maximum715
Range715
Interquartile range (IQR)68

Descriptive statistics

Standard deviation114.792928
Coefficient of variation (CV)0.9923734777
Kurtosis6.943156221
Mean115.6751269
Median Absolute Deviation (MAD)31
Skewness2.5105576
Sum68364
Variance13177.41631
2020-08-14T21:59:15.444717image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0213.6%
 
78122.0%
 
5091.5%
 
5591.5%
 
7291.5%
 
5381.4%
 
8181.4%
 
9081.4%
 
8681.4%
 
4681.4%
 
6981.4%
 
4371.2%
 
5771.2%
 
10271.2%
 
7371.2%
 
3871.2%
 
8971.2%
 
8561.0%
 
6261.0%
 
6761.0%
 
6461.0%
 
7061.0%
 
5261.0%
 
7461.0%
 
7761.0%
 
Other values (194)39366.5%
 
ValueCountFrequency (%) 
0213.6%
 
810.2%
 
1010.2%
 
1120.3%
 
1410.2%
 
1620.3%
 
1710.2%
 
2020.3%
 
2220.3%
 
2310.2%
 
ValueCountFrequency (%) 
71510.2%
 
71110.2%
 
66210.2%
 
65510.2%
 
64910.2%
 
57910.2%
 
57110.2%
 
55310.2%
 
52510.2%
 
50920.3%
 

Grade 7 Math 4s - All Students
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count96
Unique (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.41285956006768
Minimum0
Maximum304
Zeros139
Zeros (%)23.5%
Memory size29.2 KiB
2020-08-14T21:59:15.612194image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q316
95-th percentile93.5
Maximum304
Range304
Interquartile range (IQR)15

Descriptive statistics

Standard deviation38.92516206
Coefficient of variation (CV)2.114020472
Kurtosis17.76556775
Mean18.41285956
Median Absolute Deviation (MAD)4
Skewness3.837485173
Sum10882
Variance1515.168241
2020-08-14T21:59:15.783743image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
013923.5%
 
16611.2%
 
2549.1%
 
3305.1%
 
4223.7%
 
6193.2%
 
5172.9%
 
7162.7%
 
9152.5%
 
8142.4%
 
15132.2%
 
11101.7%
 
1291.5%
 
1081.4%
 
2171.2%
 
2261.0%
 
1961.0%
 
1461.0%
 
2061.0%
 
1750.8%
 
4450.8%
 
1640.7%
 
1340.7%
 
2340.7%
 
3740.7%
 
Other values (71)10217.3%
 
ValueCountFrequency (%) 
013923.5%
 
16611.2%
 
2549.1%
 
3305.1%
 
4223.7%
 
5172.9%
 
6193.2%
 
7162.7%
 
8142.4%
 
9152.5%
 
ValueCountFrequency (%) 
30410.2%
 
30310.2%
 
26310.2%
 
21410.2%
 
21310.2%
 
20510.2%
 
19810.2%
 
18810.2%
 
18010.2%
 
17010.2%
 

NumTestTakers
Real number (ℝ≥0)

ZEROS

Distinct count131
Unique (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.87478849407783
Minimum0.0
Maximum394.0
Zeros55
Zeros (%)9.3%
Memory size29.2 KiB
2020-08-14T21:59:15.957867image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median23
Q342.5
95-th percentile171.5
Maximum394
Range394
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation59.87173769
Coefficient of variation (CV)1.396432257
Kurtosis10.01898265
Mean42.87478849
Median Absolute Deviation (MAD)14
Skewness2.981148077
Sum25339
Variance3584.624973
2020-08-14T21:59:16.128549image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0559.3%
 
6193.2%
 
7172.9%
 
9172.9%
 
10162.7%
 
24152.5%
 
13152.5%
 
16152.5%
 
15152.5%
 
21142.4%
 
8142.4%
 
29132.2%
 
18132.2%
 
12132.2%
 
17132.2%
 
20132.2%
 
27122.0%
 
11122.0%
 
35122.0%
 
22111.9%
 
19101.7%
 
23101.7%
 
26101.7%
 
42101.7%
 
3871.2%
 
Other values (106)22037.2%
 
ValueCountFrequency (%) 
0559.3%
 
6193.2%
 
7172.9%
 
8142.4%
 
9172.9%
 
10162.7%
 
11122.0%
 
12132.2%
 
13152.5%
 
1471.2%
 
ValueCountFrequency (%) 
39410.2%
 
37210.2%
 
37010.2%
 
33810.2%
 
33610.2%
 
30910.2%
 
30410.2%
 
28010.2%
 
27210.2%
 
27010.2%
 

NumSpecializedOffers
Real number (ℝ≥0)

ZEROS

Distinct count55
Unique (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.798646362098139
Minimum0.0
Maximum205.0
Zeros470
Zeros (%)79.5%
Memory size29.2 KiB
2020-08-14T21:59:16.312619image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile46
Maximum205
Range205
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.41744256
Coefficient of variation (CV)3.150251008
Kurtosis31.35400083
Mean6.798646362
Median Absolute Deviation (MAD)0
Skewness5.00244774
Sum4018
Variance458.7068456
2020-08-14T21:59:16.456714image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
047079.5%
 
7142.4%
 
991.5%
 
881.4%
 
671.2%
 
2750.8%
 
2640.7%
 
2340.7%
 
1440.7%
 
1940.7%
 
1740.7%
 
5330.5%
 
2030.5%
 
1130.5%
 
4620.3%
 
1520.3%
 
2220.3%
 
2920.3%
 
2120.3%
 
1820.3%
 
9520.3%
 
1320.3%
 
1010.2%
 
11310.2%
 
15010.2%
 
Other values (30)305.1%
 
ValueCountFrequency (%) 
047079.5%
 
671.2%
 
7142.4%
 
881.4%
 
991.5%
 
1010.2%
 
1130.5%
 
1210.2%
 
1320.3%
 
1440.7%
 
ValueCountFrequency (%) 
20510.2%
 
19610.2%
 
15010.2%
 
12210.2%
 
11310.2%
 
10410.2%
 
10110.2%
 
9520.3%
 
9310.2%
 
9110.2%
 

PctBlackOrHispanic
Real number (ℝ≥0)

Distinct count88
Unique (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.08629441624366
Minimum7
Maximum100
Zeros0
Zeros (%)0.0%
Memory size29.2 KiB
2020-08-14T21:59:16.609207image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile20.5
Q161
median93
Q397
95-th percentile98
Maximum100
Range93
Interquartile range (IQR)36

Descriptive statistics

Standard deviation26.88811088
Coefficient of variation (CV)0.3488053367
Kurtosis-0.09503780027
Mean77.08629442
Median Absolute Deviation (MAD)5
Skewness-1.155014606
Sum45558
Variance722.9705068
2020-08-14T21:59:16.752124image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
987011.8%
 
976110.3%
 
96508.5%
 
95437.3%
 
94305.1%
 
99213.6%
 
93183.0%
 
92162.7%
 
91152.5%
 
87111.9%
 
90101.7%
 
6681.4%
 
2771.2%
 
4071.2%
 
8961.0%
 
6361.0%
 
8361.0%
 
2961.0%
 
7861.0%
 
6161.0%
 
1361.0%
 
3250.8%
 
2850.8%
 
3150.8%
 
7950.8%
 
Other values (63)16227.4%
 
ValueCountFrequency (%) 
720.3%
 
1020.3%
 
1130.5%
 
1220.3%
 
1361.0%
 
1430.5%
 
1540.7%
 
1610.2%
 
1740.7%
 
1910.2%
 
ValueCountFrequency (%) 
10030.5%
 
99213.6%
 
987011.8%
 
976110.3%
 
96508.5%
 
95437.3%
 
94305.1%
 
93183.0%
 
92162.7%
 
91152.5%
 

PctOffersByStudent
Real number (ℝ≥0)

ZEROS

Distinct count49
Unique (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.489001692047378
Minimum0.0
Maximum82.0
Zeros470
Zeros (%)79.5%
Memory size29.2 KiB
2020-08-14T21:59:16.914802image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile34
Maximum82
Range82
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.45435967
Coefficient of variation (CV)2.451148756
Kurtosis9.702584568
Mean5.489001692
Median Absolute Deviation (MAD)0
Skewness3.021727061
Sum3244
Variance181.0197941
2020-08-14T21:59:17.068435image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
047079.5%
 
1371.2%
 
1971.2%
 
1861.0%
 
1261.0%
 
1761.0%
 
850.8%
 
2050.8%
 
1150.8%
 
3040.7%
 
3840.7%
 
1540.7%
 
3130.5%
 
2130.5%
 
2730.5%
 
2830.5%
 
3330.5%
 
3730.5%
 
1030.5%
 
1630.5%
 
5820.3%
 
3420.3%
 
620.3%
 
720.3%
 
5720.3%
 
Other values (24)284.7%
 
ValueCountFrequency (%) 
047079.5%
 
510.2%
 
620.3%
 
720.3%
 
850.8%
 
1030.5%
 
1150.8%
 
1261.0%
 
1371.2%
 
1410.2%
 
ValueCountFrequency (%) 
8210.2%
 
7710.2%
 
7210.2%
 
7010.2%
 
6910.2%
 
6710.2%
 
6620.3%
 
6510.2%
 
6010.2%
 
5820.3%
 

TotalGrade8BlHisp
Real number (ℝ≥0)

Distinct count184
Unique (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.19966159052453
Minimum3.0
Maximum659.0
Zeros0
Zeros (%)0.0%
Memory size29.2 KiB
2020-08-14T21:59:17.223150image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile17.5
Q147
median72
Q3100
95-th percentile195
Maximum659
Range656
Interquartile range (IQR)53

Descriptive statistics

Standard deviation67.16153611
Coefficient of variation (CV)0.7882840712
Kurtosis15.91543327
Mean85.19966159
Median Absolute Deviation (MAD)26
Skewness3.098680176
Sum50353
Variance4510.671932
2020-08-14T21:59:17.387005image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
52122.0%
 
79111.9%
 
81101.7%
 
51101.7%
 
84101.7%
 
6391.5%
 
11691.5%
 
3091.5%
 
5691.5%
 
5391.5%
 
7491.5%
 
4391.5%
 
6781.4%
 
8681.4%
 
7081.4%
 
8281.4%
 
4681.4%
 
7681.4%
 
8581.4%
 
7571.2%
 
9271.2%
 
2871.2%
 
6971.2%
 
6171.2%
 
3671.2%
 
Other values (159)37763.8%
 
ValueCountFrequency (%) 
320.3%
 
410.2%
 
630.5%
 
820.3%
 
910.2%
 
1030.5%
 
1110.2%
 
1210.2%
 
1320.3%
 
1440.7%
 
ValueCountFrequency (%) 
65910.2%
 
52310.2%
 
46110.2%
 
42910.2%
 
37410.2%
 
35410.2%
 
34310.2%
 
34010.2%
 
30910.2%
 
29220.3%
 

PerDidSHSAT
Real number (ℝ≥0)

ZEROS

Distinct count86
Unique (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.952622673434856
Minimum0.0
Maximum99.0
Zeros55
Zeros (%)9.3%
Memory size29.2 KiB
2020-08-14T21:59:17.551365image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115
median26
Q342
95-th percentile72
Maximum99
Range99
Interquartile range (IQR)27

Descriptive statistics

Standard deviation21.3014153
Coefficient of variation (CV)0.7111702884
Kurtosis0.7441573344
Mean29.95262267
Median Absolute Deviation (MAD)13
Skewness0.9273757303
Sum17702
Variance453.750294
2020-08-14T21:59:17.706833image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0559.3%
 
18213.6%
 
17193.2%
 
14172.9%
 
19152.5%
 
12152.5%
 
26152.5%
 
34142.4%
 
16142.4%
 
23142.4%
 
30132.2%
 
24132.2%
 
13122.0%
 
11122.0%
 
38122.0%
 
35122.0%
 
31122.0%
 
22111.9%
 
9111.9%
 
28101.7%
 
32101.7%
 
21101.7%
 
33101.7%
 
4591.5%
 
2591.5%
 
Other values (61)22638.2%
 
ValueCountFrequency (%) 
0559.3%
 
510.2%
 
610.2%
 
761.0%
 
871.2%
 
9111.9%
 
1091.5%
 
11122.0%
 
12152.5%
 
13122.0%
 
ValueCountFrequency (%) 
9920.3%
 
9810.2%
 
9610.2%
 
9510.2%
 
9420.3%
 
9320.3%
 
9040.7%
 
8910.2%
 
8820.3%
 
8410.2%
 

AvgMark
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count159
Unique (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.558087986463621
Minimum0.0
Maximum4.04
Zeros6
Zeros (%)1.0%
Memory size29.2 KiB
2020-08-14T21:59:17.870655image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.04
Q12.24
median2.5
Q32.84
95-th percentile3.435
Maximum4.04
Range4.04
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.4966769905
Coefficient of variation (CV)0.1941594633
Kurtosis6.162338983
Mean2.558087986
Median Absolute Deviation (MAD)0.29
Skewness-0.7302153275
Sum1511.83
Variance0.2466880329
2020-08-14T21:59:18.031039image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.22132.2%
 
2.26132.2%
 
2.2132.2%
 
2.24122.0%
 
2.16111.9%
 
2.28101.7%
 
2.68101.7%
 
2.42101.7%
 
2.34101.7%
 
2.33101.7%
 
2.6491.5%
 
2.8691.5%
 
2.0681.4%
 
2.8281.4%
 
2.6681.4%
 
2.581.4%
 
2.4671.2%
 
2.0871.2%
 
2.1271.2%
 
2.1771.2%
 
2.1371.2%
 
2.7171.2%
 
2.0271.2%
 
2.4871.2%
 
2.5871.2%
 
Other values (134)36661.9%
 
ValueCountFrequency (%) 
061.0%
 
1.910.2%
 
1.9210.2%
 
1.9510.2%
 
1.9620.3%
 
1.9830.5%
 
220.3%
 
2.0120.3%
 
2.0271.2%
 
2.0310.2%
 
ValueCountFrequency (%) 
4.0410.2%
 
4.0110.2%
 
3.9910.2%
 
3.9310.2%
 
3.8610.2%
 
3.810.2%
 
3.7820.3%
 
3.7620.3%
 
3.6910.2%
 
3.6410.2%
 

PctScore4ELA
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct count53
Unique (%)9.3%
Missing20
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean9.933450087565674
Minimum0.0
Maximum71.0
Zeros69
Zeros (%)11.7%
Memory size29.2 KiB
2020-08-14T21:59:18.205321image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q313
95-th percentile34
Maximum71
Range71
Interquartile range (IQR)11

Descriptive statistics

Standard deviation12.92864839
Coefficient of variation (CV)1.301526487
Kurtosis7.106457478
Mean9.933450088
Median Absolute Deviation (MAD)4
Skewness2.499412137
Sum5672
Variance167.1499493
2020-08-14T21:59:18.388931image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
06911.7%
 
2559.3%
 
3478.0%
 
1447.4%
 
4437.3%
 
5376.3%
 
6335.6%
 
7203.4%
 
9172.9%
 
8172.9%
 
10152.5%
 
12152.5%
 
11142.4%
 
15111.9%
 
14111.9%
 
16101.7%
 
1391.5%
 
1881.4%
 
1981.4%
 
1771.2%
 
2561.0%
 
2161.0%
 
3161.0%
 
2050.8%
 
2240.7%
 
Other values (28)549.1%
 
(Missing)203.4%
 
ValueCountFrequency (%) 
06911.7%
 
1447.4%
 
2559.3%
 
3478.0%
 
4437.3%
 
5376.3%
 
6335.6%
 
7203.4%
 
8172.9%
 
9172.9%
 
ValueCountFrequency (%) 
7110.2%
 
7020.3%
 
6920.3%
 
6810.2%
 
6620.3%
 
6410.2%
 
6010.2%
 
5720.3%
 
5620.3%
 
5410.2%
 

PctScore4Math
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct count67
Unique (%)11.8%
Missing21
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean11.905263157894737
Minimum0.0
Maximum94.0
Zeros118
Zeros (%)20.0%
Memory size29.2 KiB
2020-08-14T21:59:18.577544image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q316
95-th percentile47.55
Maximum94
Range94
Interquartile range (IQR)15

Descriptive statistics

Standard deviation16.14808907
Coefficient of variation (CV)1.356382371
Kurtosis4.895140252
Mean11.90526316
Median Absolute Deviation (MAD)5
Skewness2.135389246
Sum6786
Variance260.7607807
2020-08-14T21:59:18.726260image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
011820.0%
 
1478.0%
 
4386.4%
 
2325.4%
 
3315.2%
 
5264.4%
 
12193.2%
 
8183.0%
 
6172.9%
 
11152.5%
 
7132.2%
 
9122.0%
 
10111.9%
 
14101.7%
 
17101.7%
 
1391.5%
 
2081.4%
 
1881.4%
 
2671.2%
 
1571.2%
 
2161.0%
 
2461.0%
 
1661.0%
 
3761.0%
 
2550.8%
 
Other values (42)8514.4%
 
(Missing)213.6%
 
ValueCountFrequency (%) 
011820.0%
 
1478.0%
 
2325.4%
 
3315.2%
 
4386.4%
 
5264.4%
 
6172.9%
 
7132.2%
 
8183.0%
 
9122.0%
 
ValueCountFrequency (%) 
9410.2%
 
8310.2%
 
8210.2%
 
7520.3%
 
7420.3%
 
7110.2%
 
7020.3%
 
6910.2%
 
6820.3%
 
6410.2%
 

AvgScore4
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count122
Unique (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.238578680203045
Minimum0.0
Maximum270.5
Zeros64
Zeros (%)10.8%
Memory size29.2 KiB
2020-08-14T21:59:18.886125image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4.5
Q314.5
95-th percentile82.25
Maximum270.5
Range270.5
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation33.15234124
Coefficient of variation (CV)2.041579001
Kurtosis18.60007923
Mean16.23857868
Median Absolute Deviation (MAD)4
Skewness3.891077086
Sum9597
Variance1099.07773
2020-08-14T21:59:19.033864image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
06410.8%
 
0.5549.1%
 
1447.4%
 
1.5386.4%
 
2294.9%
 
5.5183.0%
 
3172.9%
 
3.5162.7%
 
4162.7%
 
2.5162.7%
 
5162.7%
 
4.5152.5%
 
7.5122.0%
 
6.5111.9%
 
781.4%
 
9.581.4%
 
15.571.2%
 
8.571.2%
 
871.2%
 
661.0%
 
961.0%
 
1050.8%
 
1350.8%
 
11.550.8%
 
16.550.8%
 
Other values (97)15626.4%
 
ValueCountFrequency (%) 
06410.8%
 
0.5549.1%
 
1447.4%
 
1.5386.4%
 
2294.9%
 
2.5162.7%
 
3172.9%
 
3.5162.7%
 
4162.7%
 
4.5152.5%
 
ValueCountFrequency (%) 
270.510.2%
 
26510.2%
 
21010.2%
 
19010.2%
 
18710.2%
 
17810.2%
 
15810.2%
 
15610.2%
 
152.510.2%
 
14410.2%
 

PctScore4
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct count59
Unique (%)10.4%
Missing21
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean10.89298245614035
Minimum0.0
Maximum82.0
Zeros68
Zeros (%)11.5%
Memory size29.2 KiB
2020-08-14T21:59:19.190080image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q314
95-th percentile40
Maximum82
Range82
Interquartile range (IQR)12

Descriptive statistics

Standard deviation14.0271186
Coefficient of variation (CV)1.287720664
Kurtosis5.874685871
Mean10.89298246
Median Absolute Deviation (MAD)5
Skewness2.294912064
Sum6209
Variance196.7600561
2020-08-14T21:59:19.348394image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
28213.9%
 
06811.5%
 
4508.5%
 
1366.1%
 
6355.9%
 
8325.4%
 
3274.6%
 
10254.2%
 
12223.7%
 
14172.9%
 
18122.0%
 
16122.0%
 
5122.0%
 
7111.9%
 
2381.4%
 
2281.4%
 
2081.4%
 
1371.2%
 
1171.2%
 
3061.0%
 
961.0%
 
3261.0%
 
2650.8%
 
3440.7%
 
1740.7%
 
Other values (34)6010.2%
 
(Missing)213.6%
 
ValueCountFrequency (%) 
06811.5%
 
1366.1%
 
28213.9%
 
3274.6%
 
4508.5%
 
5122.0%
 
6355.9%
 
7111.9%
 
8325.4%
 
961.0%
 
ValueCountFrequency (%) 
8210.2%
 
7610.2%
 
7210.2%
 
7110.2%
 
7020.3%
 
6810.2%
 
6510.2%
 
6220.3%
 
6120.3%
 
6020.3%
 

Interactions

2020-08-14T21:57:28.356433image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:28.618693image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:28.867880image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:29.117611image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:29.360055image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:29.622959image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:29.864972image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:30.115622image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:30.349925image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:30.602007image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:30.854602image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:31.106510image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:31.347730image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:31.585370image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:31.813106image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:32.059403image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:32.293980image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:32.548600image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:32.810189image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:33.057680image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:33.304479image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:33.547074image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:33.794039image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:34.017728image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:34.269210image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:34.500097image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:34.745851image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:34.966958image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:35.207714image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:35.431175image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:35.658308image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:35.888362image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:36.129801image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:36.349541image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:36.568172image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:36.783663image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:37.019529image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:37.242281image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:37.476515image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:37.710838image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:37.928309image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:38.147903image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:38.364904image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:38.628232image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:38.875313image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:39.134864image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:39.390518image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:39.646144image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:39.888294image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:40.146322image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:40.390057image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:40.638353image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:40.892976image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:41.162873image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:41.394757image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:41.637652image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:41.876035image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:42.125346image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:42.367949image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:42.630554image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:42.894640image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:43.140325image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:43.387577image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:43.625477image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:43.860897image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:44.094155image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:44.339335image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-14T21:57:44.568797image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-14T21:59:20.059393image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-14T21:59:20.558996image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-14T21:59:21.102348image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-08-14T21:59:21.619415image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

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Sample

First rows

feeder_school_dbnfeeder_school_namecount_of_students_in_hs_admissionscount_of_testerscount_of_offersLocation CodeDistrictLatitudeLongitudePercent ELLPercent AsianPercent BlackPercent HispanicPercent Black / HispanicPercent WhiteAverage ELA ProficiencyAverage Math ProficiencyGrade 7 ELA - All Students TestedGrade 7 ELA 4s - All StudentsGrade 7 Math - All Students TestedGrade 7 Math 4s - All StudentsNumTestTakersNumSpecializedOffersPctBlackOrHispanicPctOffersByStudentTotalGrade8BlHispPerDidSHSATAvgMarkPctScore4ELAPctScore4MathAvgScore4PctScore4
001M034P.S. 034 FRANKLIN D. ROOSEVELT58.060-501M034140.726147-73.9750437%5%29%63%92%4%2.482.475545536.00.0920.053.010.02.487.05.03.56.0
101M140P.S. 140 NATHAN STRAUS67.060-501M140140.719128-73.98328314%5%13%78%90%3%2.212.274724616.00.0900.060.09.02.244.02.01.53.0
201M184P.S. 184M SHUANG WEN88.0672301M184140.711437-73.98548617%71%4%12%15%9%3.243.637222723667.023.01534.013.076.03.4431.050.029.040.0
301M188P.S. 188 THE ISLAND SCHOOL59.00-50-501M188140.719870-73.97737616%2%30%64%93%4%2.172.325405410.00.0930.055.00.02.240.02.00.51.0
401M301TECHNOLOGY, ARTS, AND SCIENCES STUDIO51.0110-501M301140.729892-73.9842316%8%29%55%84%4%2.292.0048146011.00.0840.043.022.02.142.00.00.51.0
501M332UNIVERSITY NEIGHBORHOOD MIDDLE SCHOOL68.0130-501M332140.713343-73.98606915%5%27%63%90%4%2.262.2027228013.00.0900.061.019.02.237.00.01.04.0
601M378SCHOOL FOR GLOBAL LEADERS96.0190-501M378140.720185-73.9859578%15%22%58%81%2%2.342.4869269819.00.0810.078.020.02.413.012.05.08.0
701M450EAST SIDE COMMUNITY SCHOOL94.0160-501M450140.729153-73.9824741%11%21%53%74%12%2.822.9080778916.00.0740.070.017.02.869.012.08.010.0
801M539NEW EXPLORATIONS INTO SCIENCE, TECHNOLOGY AND MATH SCHOOL136.01269101M539140.719500-73.9792390%33%9%11%20%42%3.834.031127410574126.091.02072.027.093.03.9366.070.074.068.0
901M839TOMPKINS SQUARE MIDDLE SCHOOL126.0661401M839140.723747-73.9816023%23%16%37%53%20%2.923.019322931066.014.05321.067.052.02.9624.011.016.018.0

Last rows

feeder_school_dbnfeeder_school_namecount_of_students_in_hs_admissionscount_of_testerscount_of_offersLocation CodeDistrictLatitudeLongitudePercent ELLPercent AsianPercent BlackPercent HispanicPercent Black / HispanicPercent WhiteAverage ELA ProficiencyAverage Math ProficiencyGrade 7 ELA - All Students TestedGrade 7 ELA 4s - All StudentsGrade 7 Math - All Students TestedGrade 7 Math 4s - All StudentsNumTestTakersNumSpecializedOffersPctBlackOrHispanicPctOffersByStudentTotalGrade8BlHispPerDidSHSATAvgMarkPctScore4ELAPctScore4MathAvgScore4PctScore4
58184X491ACADEMIC LEADERSHIP CHARTER SCHOOL83.0320-584X491740.807731-73.91276810%0%46%52%98%1%2.833.114810492132.00.0980.081.039.02.9721.043.015.532.0
58284X492SOUTH BRONX EARLY COLLEGE ACADEMY CHARTER SCHOOL115.0110-584X492740.808913-73.92174614%0%34%63%97%1%0.000.00000011.00.0970.0112.010.00.00NaNNaN0.0NaN
58384X493SUCCESS ACADEMY CHARTER SCHOOL - BRONX 147.0300-584X493740.813660-73.9259996%1%56%40%96%1%3.233.97000030.00.0960.045.064.03.60NaNNaN0.0NaN
58484X494SUCCESS ACADEMY CHARTER SCHOOL - BRONX 265.036684X494940.835973-73.9048387%1%63%34%98%1%3.163.91000036.06.09817.064.055.03.54NaNNaN0.0NaN
58584X496ICAHN CHARTER SCHOOL 433.0290-584X4961140.856635-73.8430420%2%61%33%95%1%3.003.522715271029.00.0950.031.088.03.2656.037.012.546.0
58684X538ICAHN CHARTER SCHOOL 527.0200-584X5381140.856635-73.8430421%4%56%31%87%8%3.033.18000020.00.0870.023.074.03.10NaNNaN0.0NaN
58784X703BRONX PREPARATORY CHARTER SCHOOL114.0220-584X703940.839046-73.9001907%1%50%48%98%1%2.392.5014691441322.00.0980.0112.019.02.456.09.011.08.0
58884X704KIPP ACADEMY CHARTER SCHOOL77.0230-584X704740.816194-73.9261698%0%40%58%98%1%2.422.8772372723.00.0980.075.030.02.644.010.05.07.0
58984X706HARRIET TUBMAN CHARTER SCHOOL69.0240-584X706940.832272-73.90581711%0%68%31%98%1%2.502.8564264724.00.0980.068.035.02.683.011.04.57.0
59084X717ICAHN CHARTER SCHOOL29.0240-584X717940.839176-73.9049822%0%54%45%99%1%2.773.0934234624.00.0990.029.083.02.936.018.04.012.0